Multi-objective optimization design method and system for permanent magnet motor

Abstract

The invention discloses a multi-objective optimization design method and system for a permanent magnet motor, and belongs to the field of permanent magnet motors. According to the method, under the condition that the number of optimization parameters and the number of parameter levels are given, a Taguchi experiment method is adopted. The number of times of simulation (experiment) can be greatly reduced. That is, an analysis mode of one-time simulation of one scheme does not need to be adopted, and the time required to be consumed in the motor optimization process is effectively shortened. onthe basis, the RBF network is adopted to establish a nonlinear relationship between motor structure parameters and performance indexes of the motor structure parameters, and a discrete solution domainis converted into a continuous solution domain, so that the accuracy of subsequent optimization can be ensured. According to the invention, the fuzzy optimal worst method is used to obtain the weightcoefficient of each performance index, so that the multi-objective problem is converted into the single-objective problem to be analyzed and optimized more reasonably.

Description

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AI Technical Summary

Benefits of technology

This technology allows for better understanding about optimizing multiple objectives simultaneously without requiring many experimental runs or adjustments from different values. By utilising both methods together, this approach helps identify an overall best way to solve complex multimodal issues such as minimization of stress concentration and deformation during manufacture processes while also improving productivity efficiency through faster production times.

Problems solved by technology

Technological Problem: The technical problem addressed in this patented text relates to improving the speed and energy output (SPE) capacity of rare earth magnetic material based machines due to factors like temperature changes or loadings caused during use. Current methods involve trial-and-error techniques which require significant amounts of computational resources and result in slow convergence times. Therefore there needs an improved solution called Multi Objectives Optimization Design Methods(MOP), specifically Quantum Algorithms Perturbation Theory (QCAT).

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